DocumentCode
2778489
Title
Creating 3D models with uncalibrated cameras
Author
Han, Mei ; Kanade, Takeo
Author_Institution
Robotics Inst., Carnegie Mellon Univ., Pittsburgh, PA, USA
fYear
2000
fDate
2000
Firstpage
178
Lastpage
185
Abstract
We describe a factorization-based method to recover 3D models from multiple perspective views with uncalibrated cameras. The method first performs a projective reconstruction using a bilinear factorization algorithm, and then converts the projective solution to a Euclidean one by enforcing metric constraints. We present three factorization-based normalization algorithms to generate the Euclidean reconstruction and the intrinsic parameters, assuming zero skews. The first two algorithms are linear, one for dealing with the case that only the focal lengths are unknown, and another for the case that the focal lengths and the constant principal point are unknown. The third algorithm is bilinear dealing with the case that the focal lengths, the principal points and the aspect ratios are all unknown. We present the results of applying this method to building modeling, terrain recovery and multi-camera calibration
Keywords
image reconstruction; terrain mapping; 3D models; Euclidean reconstruction; bilinear factorization; building modeling; intrinsic parameters; multi-camera calibration; multiple perspective views; projective reconstruction; terrain recovery; uncalibrated cameras; Calibration; Cameras; Image reconstruction; Image sequences; Iterative algorithms; Robot vision systems; Robustness; Shape; Singular value decomposition; Subspace constraints;
fLanguage
English
Publisher
ieee
Conference_Titel
Applications of Computer Vision, 2000, Fifth IEEE Workshop on.
Conference_Location
Palm Springs, CA
Print_ISBN
0-7695-0813-8
Type
conf
DOI
10.1109/WACV.2000.895420
Filename
895420
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